Fine Mapping:
Parkinson’s Disease & Alzheimer’s Disease
Parkinson’s Disease & Alzheimer’s Disease
Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York
RAJ LAB 
Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience
Icahn School of Medicine at Mount Sinai
NYC, New York
RAJ LAB
2019-03-06
Setup
# devtools::install_local("geneRefineR/", force = T)
library("geneRefineR")
library(readxl)
library(DT)
library(data.table)
library(dplyr)
library(ggplot2)
library(plotly)
library(cowplot)
library(ggrepel)
library(curl)
library(biomaRt)
library(sqldf)
# Ensembl LD API
library(httr)
library(jsonlite)
library(xml2)
library(gaston)
library(RCurl)
# *** susieR ****
# library(knitrBootstrap) #install_github('jimhester/knitrBootstrap')
library(susieR) # devtools::install_github("stephenslab/susieR")
sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.14.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] susieR_0.6.2.0411 RCurl_1.95-4.11 bitops_1.0-6
## [4] gaston_1.5.4 RcppParallel_4.4.2 Rcpp_1.0.0
## [7] xml2_1.2.0 jsonlite_1.6 httr_1.4.0
## [10] sqldf_0.4-11 RSQLite_2.1.1 gsubfn_0.7
## [13] proto_1.0.0 biomaRt_2.38.0 curl_3.3
## [16] ggrepel_0.8.0 cowplot_0.9.4 plotly_4.8.0
## [19] ggplot2_3.1.0 dplyr_0.8.0.1 data.table_1.12.0
## [22] DT_0.5.2 readxl_1.3.0 geneRefineR_0.0.0.9000
##
## loaded via a namespace (and not attached):
## [1] lattice_0.20-38 tidyr_0.8.2 prettyunits_1.0.2
## [4] assertthat_0.2.0 digest_0.6.18 R6_2.4.0
## [7] cellranger_1.1.0 plyr_1.8.4 chron_2.3-53
## [10] stats4_3.5.1 evaluate_0.13 pillar_1.3.1
## [13] rlang_0.3.1 progress_1.2.0 lazyeval_0.2.1
## [16] blob_1.1.1 S4Vectors_0.20.1 Matrix_1.2-15
## [19] rmarkdown_1.11 stringr_1.4.0 htmlwidgets_1.3
## [22] bit_1.1-14 munsell_0.5.0 compiler_3.5.1
## [25] xfun_0.5 pkgconfig_2.0.2 BiocGenerics_0.28.0
## [28] htmltools_0.3.6 tcltk_3.5.1 tidyselect_0.2.5
## [31] expm_0.999-3 tibble_2.0.1 IRanges_2.16.0
## [34] XML_3.98-1.17 viridisLite_0.3.0 crayon_1.3.4
## [37] withr_2.1.2 grid_3.5.1 gtable_0.2.0
## [40] DBI_1.0.0 magrittr_1.5 scales_1.0.0
## [43] stringi_1.3.1 tools_3.5.1 bit64_0.9-7
## [46] Biobase_2.42.0 glue_1.3.0 purrr_0.3.0
## [49] hms_0.4.2 parallel_3.5.1 yaml_2.2.0
## [52] AnnotationDbi_1.44.0 colorspace_1.4-0 memoise_1.1.0
## [55] knitr_1.21print(paste("susieR ", packageVersion("susieR")))## [1] "susieR 0.6.2.411"Finemap PD Genes
list[top_SNPs, SumStats_sig] <- import_sig_GWAS(
file_path = "Data/Parkinsons/Nalls2018_S2_SummaryStats.xlsx",
sheet="Data",
chrom_col = "CHR", position_col = "BP", snp_col="SNP",
pval_col="P, all studies", effect_col="Beta, all studies", gene_col="Nearest Gene",
caption= "Nalls et al. (2018) PD GWAS Summary Stats")
finemapped_PD <- finemap_geneList(top_SNPs, geneList=c("LRRK2","GBAP1","SNCA","VPS13C","GCH1"), #unique(top_SNPs$Gene)
filePath="Data/Parkinsons/META.PD.NALLS2014.PRS.tsv",
snp_col = "MarkerName", pval_col = "P.value")LRRK2
Extracting SNPs flanking lead SNP…
Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-4890.90196469039” [1] “objective:-4890.86512255644” [1] “objective:-4890.86493793192” [1] “objective:-4890.86493700424”
## Warning in log(susieDF$Effect): NaNs produced
## Warning in log(susieDF$Effect): NaNs produced## Warning: Removed 1 rows containing missing values (geom_hline).
1 / 10 (10%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.
GBAP1
Extracting SNPs flanking lead SNP…
Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-1768.43682307539” [1] “objective:-1767.99485312494” [1] “objective:-1767.99377794051” [1] “objective:-1767.99377530418” [1] “objective:-1767.99377529783” [1] “objective:-1767.99377529781”
## Warning in log(susieDF$Effect): NaNs produced## Warning in log(susieDF$Effect): NaNs produced## Warning: Removed 1 rows containing missing values (geom_hline).
0 / 10 (0%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.
SNCA
Extracting SNPs flanking lead SNP…
Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-3587.57986639256” [1] “objective:-3584.84439532171” [1] “objective:-3584.84224690147” [1] “objective:-3584.84224525982” [1] “objective:-3584.84224525865” [1] “objective:-3584.84224525865”
## Warning in log(susieDF$Effect): NaNs produced## Warning in log(susieDF$Effect): NaNs produced
2 / 10 (20%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.
VPS13C
Extracting SNPs flanking lead SNP…
Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-3928.09335227999” [1] “objective:-3928.05140701022” [1] “objective:-3928.05134302287” [1] “objective:-3928.05134292561”
## Warning in log(susieDF$Effect): NaNs produced
## Warning in log(susieDF$Effect): NaNs produced## Warning: Removed 1 rows containing missing values (geom_hline).
1 / 10 (10%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.
GCH1
Extracting SNPs flanking lead SNP…
Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-2952.66209303207” [1] “objective:-2952.62332682449” [1] “objective:-2952.62312855485” [1] “objective:-2952.62312754298”
## Warning in log(susieDF$Effect): NaNs produced## Warning in log(susieDF$Effect): NaNs produced## Warning: Removed 1 rows containing missing values (geom_hline).
0 / 10 (0%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.
Finemap AD Genes
list[top_SNPs, SumStats_sig] <- import_sig_GWAS(
file_path = "Data/Alzheimers/Posthuma/AD_target_SNP.xlsx",
sheet = 3,
chrom_col = "Chr", position_col = "bp", snp_col="SNP",
pval_col="P", effect_col="Z", gene_col="Gene",
caption= "Posthuma et al. (2018) AD GWAS Summary Stats")
finemapped_AD <- finemap_geneList(top_SNPs, geneList=c("CLU/PTK2B","APOE"), #unique(top_SNPs$Gene)
filePath="Data/Alzheimers/Posthuma/phase3.beta.se.hrc.txt",
effect_col = "BETA", stderr_col = "SE", position_col = "BP")CLU/PTK2B
- Extracting SNPs flanking lead SNP…
## Warning: NAs introduced by coercion
## Warning: NAs introduced by coercionCreating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
Fine mapping with SusieR… [1] “objective:-6159.30949844661” [1] “objective:-6158.9541676718” [1] “objective:-6158.95404180037” [1] “objective:-6158.95404175575”
## Warning in log(susieDF$Effect): NaNs produced## Warning in log(susieDF$Effect): NaNs produced
1 / 10 (10%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.
APOE
- Extracting SNPs flanking lead SNP…
## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion
+ Creating LD matrix… LD Reference Panel = 1KG_Phase1 ped stats and snps stats have been set. ‘p’ has been set. ‘mu’ and ‘sigma’ have been set.
- Fine mapping with SusieR… [1] “objective:-4950.18491679679” [1] “objective:-4721.83314537539” [1] “objective:-4721.71980975089” [1] “objective:-4721.71975448281” [1] “objective:-4721.71975446467” [1] “objective:-4721.71975446467”
## Warning in log(susieDF$Effect): NaNs produced## Warning in log(susieDF$Effect): NaNs produced
0 / 10 (0%) of SNPs of the SNPs in the summary stats were confirmed after fine-mapping.